Apoptosis is a highly conserved, continuous physiological process for non-inflammatory cell death, and the dysregulation of apoptosis has been shown to contribute to the initiation and progression of the tumorigenesis process. Variations in apoptosis capacity are expected to influence an individual's risk and progression of cancers, and an improved understanding of the variation in apoptosis capacity between individuals is likely to be beneficial in the prognosis and treatment of various diseases. However, apoptosis is a complex process that involves hundreds of proteins and is composed of multiple levels of redundancy, which makes it is difficult and tedious to dissect the major players that determine apoptosis capacity at the molecular level by in vitro experiments and animal models. Here we propose an alternative approach to pinpoint the major determinants of apoptosis through the identification of quantitative trait loci (QTL) that determine apoptosis capacity using a linkage analysis in a collection of informative families. These apoptosis QTL will then be followed up by a positional candidate gene approach that focuses only on the functionally relevant genes in the target chromosomal regions, thus quickly narrowing down the search to genes that contribute to variations in apoptosis capacity and are most directly relevant to human diseases in the general population. In this grant application, we propose to investigate the genetic variations and determinants of apoptosis capacity, using a large collection of 188 hereditary prostate cancer (HPC) families. Using an existing genome-wide scan marker dataset, we can systematically identify chromosomal regions likely to contain genes responsible for determining individuals'apoptosis capacity, in contrast to subjective selection of specific genes for evaluation. Furthermore, we can compare the results from genome-wide screens for apoptosis capacity with the results from our previous genome-wide screens for hereditary prostate cancer, clinically significant prostate cancer, and all types of cancers to identify apoptosis genes that have the greatest impact on cancer susceptibility. Our proposal describes a novel and efficient approach to identify apoptosis genes that are critical in cancer susceptibility.